Description

1. Linear Regression Model:

   • Explanation of the linear, additive feature-to-target relationship.
   • Famous regression equation in the context of resale price forecasting.
   • Visualization of how regression models explain variation in target variables.

2. Regression Model Estimation:

   • Understanding parametric models, loss functions, and residuals.
   • Steps to determine free parameters and optimize model fit.
   • Intuition behind regression model estimation using concepts like equilibrium solutions.

3. Least Squares Loss Function:

   • Definition and calculation of the squared-error loss.
   • Formalization of model estimation and finding optimal solutions.
   • Example of linear regression with a squared-error loss function.

4. Supervised Learning Algorithm:

   • Model specification and estimation for continuous target variables.
   • The two-stage paradigm in supervised machine learning.
   • Differences between empirical risk minimization and structural risk minimization.

5. Dual Role of Linear Regression:

   • Linear regression as both an explanatory and predictive model.
   • Practical applications in forecasting and understanding feature-target relationships.

Learning Outcomes

By the end of this section, you will be able to:

1. Understand Linear Regression:

   • Grasp the fundamentals of linear regression models and their assumptions.

2. Estimate Regression Models:

   • Learn how to determine free parameters and optimize model fit using loss functions.

3. Apply Regression in Business Contexts:

   • Utilize linear regression for practical applications like resale price forecasting.

4. Comprehend Supervised Learning:

   • Understand the two-stage paradigm and its significance in machine learning.

5. Differentiate Model Roles:

   • Recognize the explanatory and predictive capabilities of linear regression models.

Recommendations

There are no strict prerequisites for starting this section. However, continuous engagement and completing the videos in the recommended section along with the provided PDFs will significantly enhance your understanding of predictive analytics.Upon completion, the next recommended section is "Foundations of Predictive Analytics - Part 3". This will build on the knowledge gained in Part 1 and 2, delving deeper into advanced topics and further practical applications of predictive analytics.Actively participate and practice the concepts learned to apply predictive analytics effectively in real-world scenarios.